Laguna by Poolside vs MiMo-V2-Flash: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and MiMo-V2-Flash — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.
MiMo-V2-Flash
XiaomiMiMo
MiMo-V2-Flash is a MiMo family language-model variant focused on improving reasoning capabilities through pretraining-to-posttraining methods.
Key features
- Pretraining Recipes: Provides documented workflows and scripts for model pretraining to establish baseline capabilities and training reproducibility.
- Posttraining Techniques: Includes methods and guidelines for posttraining interventions aimed at improving reasoning or task-specific performance after initial pretraining.
- Model Variant (MiMo-V2-Flash): Supplies a specific model configuration within the MiMo family optimized for reasoning and efficient inference.
- Evaluation and Benchmarks: Offers evaluation code and benchmark suites to measure reasoning quality and compare model variants across tasks.
- Open-Source Implementation: Publishes code, experiment configuration, and reproducible pipelines to enable researchers to replicate results and extend the project.
- Fine-tuning Guidance: Provides instructions and scripts to adapt base models to downstream tasks or specialized domains using the MiMo posttraining approach.
- Repository of research code for improving reasoning capabilities of language models
- Pretraining and posttraining methodologies and scripts
- Model checkpoints and release artifacts (where provided in repo)
- Evaluation and benchmarking scripts for reasoning tasks
- Documentation and usage examples for reproducibility
Best for
- Research on reasoning capabilities: Use MiMo-V2-Flash to study, benchmark, and iterate on methods that improve chain-of-thought and multi-step reasoning in LLMs.
- Model fine-tuning for domain tasks: Apply provided training and posttraining recipes to adapt the model for domain-specific applications like technical QA or summarization.
- Reproducible experimentation: Reproduce published MiMo experiments and extend them by changing datasets, hyperparameters, or posttraining strategies.
- Benchmarking and comparison: Evaluate MiMo-V2-Flash against other LLM variants across standardized reasoning and inference benchmarks.
- Prototype inference-optimized deployments: Use the MiMo-V2-Flash variant as a base for latency-sensitive or resource-constrained inference setups requiring strong reasoning behavior.
- Educational use and method demonstration: Learn end-to-end model development from pretraining through posttraining using the open-source repository and example scripts.
- Research on improving multi-step reasoning in LMs
- Fine-tuning and posttraining experiments on reasoning datasets
- Benchmarking and evaluation of model reasoning capabilities
- Reproducing and building on published MiMo research
- Integrating released checkpoints into downstream applications for improved reasoning